We adopt cross-validation to evaluate the performance.
For Adressa, we split the user-item interactions into the training set, validation set, and test set according to the ratio of 8:1:1 in chronological order due to the timeliness of news [1]}. As for the other two datasets, we randomly split the historical interactions according to the ratio of 8:1:1. After that, the clean test set is constructed for each dataset as described in section REF .
The performance is measured by two widely used top-\(K\)  recommendation metrics [2]}, [3]}: recall@\(K\)  and ndcg@\(K\) . By default, we set K = 5, 20 for Modcloth and Adressa, and K = 10, 50 for Electronics since the number of items in Electronics is larger. All experiments are run 3 times and the average is reported.
